• Title/Summary/Keyword: Hybrid Recommendation

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Mobile App Recommendation using User's Spatio-Temporal Context (사용자의 시공간 컨텍스트를 이용한 모바일 앱 추천)

  • Kang, Younggil;Hwang, Seyoung;Park, Sangwon;Lee, Soowon
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.9
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    • pp.615-620
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    • 2013
  • With the development of smartphones, the number of applications for smartphone increases sharply. As a result, users need to try several times to find their favorite apps. In order to solve this problem, we propose a recommendation system to provide an appropriate app list based on the user's log information including time stamp, location, application list, and so on. The proposed approach learns three recommendation models including Naive-Bayesian model, SVM model, and Most-Frequent Usage model using temporal and spatial attributes. In order to figure out the best model, we compared the performance of these models with variant features, and suggest an hybrid method to improve the performance of single models.

Consumption and Conversion Efficiency of Food in New Elite Bivoltine Hybrid Silkworm, Bombyx mori L. under Restricted Feeding Levels

  • Mathur, Vinod B.;Rahmathulla, V.K.;Bhaskar, O.Vijaya
    • International Journal of Industrial Entomology and Biomaterials
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    • v.5 no.2
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    • pp.213-216
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    • 2002
  • Food consumption and conversion efficiency of new bivoltine hybrids (CSR2$\times$CSR4 and CSR2$\times$CSR5) were studied under restricted feeding levels (10, 20 and 30% less quantity of mulberry leaves). The data were compared with a control fed with standard quantum of feed as per the recommendation. The nutritional indices parameters i. e. ingests, digesta, approximate digestibility (%) and reference ratio were recorded higher in control batches compared to less feed batches while nutritional efficiency parameters i. e., ECI and ECD to cocoon and shell were recorded significantly higher in restricted feeding level batches. This increase is attributed due to the physiological adaptation under nutritional stress condition.

Development of Web-based Intelligent Recommender Systems using Advanced Data Mining Techniques (개선된 데이터 마이닝 기술에 의한 웹 기반 지능형 추천시스템 구축)

  • Kim Kyoung-Jae;Ahn Hyunchul
    • Journal of Information Technology Applications and Management
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    • v.12 no.3
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    • pp.41-56
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    • 2005
  • Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

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Comparative Analysis on Insulation Performance of Traction Motors for a Hybrid Vehicle (하이브리드 차량용 견인전동기의 절연성능 비교분석)

  • Park, Dae-Won;Park, Chan-Yong;Kil, Gyung-Suk;Lee, Kang-Won
    • Proceedings of the KSR Conference
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    • 2008.11b
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    • pp.219-224
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    • 2008
  • The purpose of this paper is to acquire the data related to insulation evaluation of hybrid vehicle traction motors. We carried out a comparative analysis on Insulation Resistance (IR) and Polarization Index (PI) of motor stators according to IEEE Std. 43 and IEC 60085-1 for insulation resistance test standard of rotating machinery. Maximum test voltage which is applied to between a phase and enclosure was set at 500 V. The IRs of the used motors were lower than those of the new ones. The PIs of the used motor were ranges from 0.74 to 1.1 and did not meet the recommendation basis 2 for insulation level H. From the experimental results, we could prepare parameters and basis for insulation evaluation of motor stators by comparative analysis of the IR and the PI.

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Scalable Collaborative Filtering Technique based on Adaptive Clustering (적응형 군집화 기반 확장 용이한 협업 필터링 기법)

  • Lee, O-Joun;Hong, Min-Sung;Lee, Won-Jin;Lee, Jae-Dong
    • Journal of Intelligence and Information Systems
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    • v.20 no.2
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    • pp.73-92
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    • 2014
  • An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

Comparative Analysis on Insulation Performance of Traction Motors for Hybrid Vehicles (하이브리드 차량용 견인전동기의 절연성능 비교분석)

  • Choi, Su-Yeon;Park, Chan-Yong;Kim, Sung-Wook;Park, Dae-Won;Kil, Gyung-Suk;Lee, Kang-Won
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
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    • v.21 no.12
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    • pp.1124-1129
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    • 2008
  • The object of this paper is to acquire the data related to insulation evaluation of hybrid vehicle traction motors. We made a comparative analysis on Insulation Resistance (IR), Dielectric Absorption Ratio (DAR), and Polarization Index (PI) of the motor stators. The experiment was carried out according to IEEE Std. 43 and IEC 60085-1 for insulation resistance test standard of rotating machinery. Test voltage of 500 V was applied between a phase and the enclosure. The IR and the DAR of used motors were lower than those of new ones. The DAR and the PI were $0.92{\sim}1.02$ and $0.74{\sim}1.1$, respectively and the result did not meet the recommendation basis 2 for insulation level H. From the experimental results, we could prepare parameters and basis for insulation evaluation of the traction motor stator by the comparative analysis of short-time insulation resistance changes, DAR and PI.

Role of Phytoecdysteroid Treatment Time in the Maturation Process of $Multi{\times}Bivoltine$ ($BL67{\times}CSR101$) Hybrid Silkworm, Bombyx mori L. When Maintained at Low, Medium and High Temperature

  • Kumar S. Nirmal;Nair K. Sashindran;Rabha Jagat
    • International Journal of Industrial Entomology and Biomaterials
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    • v.12 no.2
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    • pp.51-56
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    • 2006
  • Use of products containing phytoecdysteroid (PE) as active principle has become popular in prominent sericultural areas of India for hastening larval maturation events and synchronizing cocoon spinning activities as an obvious advantage is assured. At times, the present recommendation of administering PE at the onset of spinning results in peak labour requirement at odd hrs. To enable making recommendation for the use of PE on $multi{\times}bivoltine$ silkworm hybrids based on the climatic conditions prevailing in different areas especially with regard to temperature, the experiment was taken up to determine proper treatment times so that the induced spinning will be more orderly and the labour can be leveraged more efficiently. Different brackets of low ($18-22^{\circ}C$), medium ($24-28^{\circ}C$) and high ($29-32^{\circ}C$) temperature were simulated during the latter half of V larval instar and cocoon spinning. PE was administered to $multi{\times}bivoltine$ silkworm ($BL67{\times}CSR101$) hybrid batches as per the recommended dose at three different times viz., 10 am, 4 pm and 10 pm. Three replicates of 100 larvae were maintained for each treatment. Absolute controls were also maintained in each temperature range to compare the results. Cumulative maturation percentage was recorded at 6 hrs interval to ascertain peak mounting span. The influence of the treatment on the cocoon traits also was studied. Based on the peak mounting span, it was evident that in low temperature 10 pm treatment would be better. In medium and high temperature, treatment at 4 pm proved to be a better option. The influence of the treatment times at different temperature range on labour management is discussed.

AHP와 하이브리드 필터링을 이용한 개인화된 추천 시스템 설계 및 구현

  • Kim, Su-Yeon;Lee, Sang Hoon;Hwang, Hyun-Seok
    • Journal of Korea Society of Industrial Information Systems
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    • v.17 no.7
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    • pp.111-118
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    • 2012
  • Recently, most of firms have continuously released new products satisfying various needs of customers in order to increase market share. As a lot of products with various functionalities, prices and designs are released in the market, users have difficulties in choosing an appropriate product, especially for information technology driven devices. In case of digital cameras, inexperienced users spend a lot of time and efforts to find proper model for them. In this study, therefore, we design and implement a personalized recommendation system using analytic hierarchy process, one of the multi-criteria decision making techniques, and hybrid filtering combining content-based filtering and collaborative filtering to recommend a suitable product for inexperienced users of information technology devices.

A Hybrid Collaborative Filtering-based Product Recommender System using Search Keywords (검색 키워드를 활용한 하이브리드 협업필터링 기반 상품 추천 시스템)

  • Lee, Yunju;Won, Haram;Shim, Jaeseung;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.26 no.1
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    • pp.151-166
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    • 2020
  • A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

A Study on Improvement of Collaborative Filtering Based on Implicit User Feedback Using RFM Multidimensional Analysis (RFM 다차원 분석 기법을 활용한 암시적 사용자 피드백 기반 협업 필터링 개선 연구)

  • Lee, Jae-Seong;Kim, Jaeyoung;Kang, Byeongwook
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.139-161
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    • 2019
  • The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.